Retrieval Augmented Generation (RAG) has not only gained steam as one of the most invested areas of research in generative AI but also gathered considerable popularity and commercialization opportunities. RAG is typically applied to question-answering problems, where certain external contextual information retrieved from a data source (potentially private) is provided